A novel segmentation method to identify left ventricular infarction in short-axis composite strain-encoded magnetic resonance images

Ahmad O. Algohary, Muhammad K. Metwally, Ahmed M. El-Bialy, Ahmed H. Kandil, Nael F. Osman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Composite Strain Encoding (CSENC) is a new Magnetic Resonance Imaging (MRI) technique for simultaneously acquiring cardiac functional and viability images. It combines the use of Delayed Enhancement (DE) and the Strain Encoding (SENC) imaging techniques to identify the infracted (dead) tissue and to image the myocardial deformation inside the heart muscle. In this work, a new unsupervised segmentation method is proposed to identify infarcted left ventricular tissue in the images provided by CSENC MRI. The proposed method is based on the sequential application of Bayesian classifier, Otsu's thresholding, morphological opening, radial sweep boundary tracing and the fuzzy C-means (FCM) clustering algorithm. This method is tested on images of twelve patients with and without myocardial infarction (MI) and on simulated heart images with various levels of superimposed noise. The resulting clustered images are compared with those marked up by an expert cardiologist who assisted in validating results coming from the proposed method. Infarcted myocardium is correctly identified using the proposed method with high levels of accuracy and precision.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2011
Subtitle of host publicationImage Processing
DOIs
StatePublished - Jun 9 2011
EventMedical Imaging 2011: Image Processing - Lake Buena Vista, FL, United States
Duration: Feb 14 2011Feb 16 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7962
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2011: Image Processing
CountryUnited States
CityLake Buena Vista, FL
Period2/14/112/16/11

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Keywords

  • CSENC
  • Cardiac MRI
  • FCM
  • Heart
  • Infarct
  • Left Ventricle
  • Segmentation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Algohary, A. O., Metwally, M. K., El-Bialy, A. M., Kandil, A. H., & Osman, N. F. (2011). A novel segmentation method to identify left ventricular infarction in short-axis composite strain-encoded magnetic resonance images. In Medical Imaging 2011: Image Processing [79622E] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 7962). https://doi.org/10.1117/12.877098